





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = KandinskyVaaInpaintPipeline
      _SCREAMING_SNAKE_CASE							      = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _SCREAMING_SNAKE_CASE							      = [
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _SCREAMING_SNAKE_CASE							      = [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _SCREAMING_SNAKE_CASE							      = False





      @property
      def        _snake_case     (			self					)						->	Union[str, Any]:
            return 32





      @property
      def        _snake_case     (			self					)						->	Optional[Any]:
            return 32





      @property
      def        _snake_case     (			self					)						->	Tuple:
            return self.time_input_dim





      @property
      def        _snake_case     (			self					)						->	Dict:
            return self.time_input_dim * 4





      @property
      def        _snake_case     (			self					)						->	int:
            return 100





      @property
      def        _snake_case     (			self					)						->	Optional[int]:
            torch.manual_seed(0					)

            lowerCAmelCase    		=			{
                """in_channels""": 9,
                # Out channels is double in channels because predicts mean and variance
                """out_channels""": 8,
                """addition_embed_type""": """image""",
                """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
                """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
                """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
                """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
                """layers_per_block""": 1,
                """encoder_hid_dim""": self.text_embedder_hidden_size,
                """encoder_hid_dim_type""": """image_proj""",
                """cross_attention_dim""": self.cross_attention_dim,
                """attention_head_dim""": 4,
                """resnet_time_scale_shift""": """scale_shift""",
                """class_embed_type""": None,
            }

            lowerCAmelCase    		=			UNetaDConditionModel(**lowercase					)
            return model





      @property
      def        _snake_case     (			self					)						->	Optional[Any]:
            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 12,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }





      @property
      def        _snake_case     (			self					)						->	List[Any]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			VQModel(**self.dummy_movq_kwargs					)
            return model





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			self.dummy_unet
            lowerCAmelCase    		=			self.dummy_movq

            lowerCAmelCase    		=			DDIMScheduler(
                num_train_timesteps=1_000 ,		beta_schedule="""linear""" ,		beta_start=0.00_085 ,		beta_end=0.012 ,		clip_sample=lowercase ,		set_alpha_to_one=lowercase ,		steps_offset=1 ,		prediction_type="""epsilon""" ,		thresholding=lowercase ,		)

            lowerCAmelCase    		=			{
                """unet""": unet,
                """scheduler""": scheduler,
                """movq""": movq,
            }

            return components





      def        _snake_case     (			self ,		lowercase ,		lowercase=0					)						->	List[str]:
            lowerCAmelCase    		=			floats_tensor((1, self.text_embedder_hidden_size) ,		rng=random.Random(lowercase					)					).to(lowercase					)
            lowerCAmelCase    		=			floats_tensor((1, self.text_embedder_hidden_size) ,		rng=random.Random(seed + 1					)					).to(
                lowercase					)
            # create init_image
            lowerCAmelCase    		=			floats_tensor((1, 3, 64, 64) ,		rng=random.Random(lowercase					)					).to(lowercase					)
            lowerCAmelCase    		=			image.cpu().permute(0 ,		2 ,		3 ,		1					)[0]
            lowerCAmelCase    		=			Image.fromarray(np.uinta(lowercase					)					).convert("""RGB"""					).resize((256, 256)					)
            # create mask
            lowerCAmelCase    		=			np.ones((64, 64) ,		dtype=np.floataa					)
            lowerCAmelCase    		=			0

            if str(lowercase					).startswith("""mps"""					):
                  lowerCAmelCase    		=			torch.manual_seed(lowercase					)
            else:
                  lowerCAmelCase    		=			torch.Generator(device=lowercase					).manual_seed(lowercase					)
            lowerCAmelCase    		=			{
                """image""": init_image,
                """mask_image""": mask,
                """image_embeds""": image_embeds,
                """negative_image_embeds""": negative_image_embeds,
                """generator""": generator,
                """height""": 64,
                """width""": 64,
                """num_inference_steps""": 2,
                """guidance_scale""": 4.0,
                """output_type""": """np""",
            }
            return inputs





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			"""cpu"""

            lowerCAmelCase    		=			self.get_dummy_components()

            lowerCAmelCase    		=			self.pipeline_class(**lowercase					)
            lowerCAmelCase    		=			pipe.to(lowercase					)

            pipe.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			pipe(**self.get_dummy_inputs(lowercase					)					)
            lowerCAmelCase    		=			output.images

            lowerCAmelCase    		=			pipe(
                **self.get_dummy_inputs(lowercase					) ,		return_dict=lowercase ,		)[0]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            print(f'image.shape {image.shape}'					)

            assert image.shape == (1, 64, 64, 3)

            lowerCAmelCase    		=			np.array(
                [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]					)

            assert (
                np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
            ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





      def        _snake_case     (			self					)						->	Any:
            super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







@slow
@require_torch_gpu
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Any:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""					)

            lowerCAmelCase    		=			load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
            lowerCAmelCase    		=			np.ones((768, 768) ,		dtype=np.floataa					)
            lowerCAmelCase    		=			0

            lowerCAmelCase    		=			"""a hat"""

            lowerCAmelCase    		=			KandinskyVaaPriorPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-prior""" ,		torch_dtype=torch.floataa					)
            pipe_prior.to(lowercase					)

            lowerCAmelCase    		=			KandinskyVaaInpaintPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-2-decoder-inpaint""" ,		torch_dtype=torch.floataa					)
            lowerCAmelCase    		=			pipeline.to(lowercase					)
            pipeline.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.Generator(device="""cpu"""					).manual_seed(0					)
            lowerCAmelCase		,						lowerCAmelCase    		=			pipe_prior(
                lowercase ,		generator=lowercase ,		num_inference_steps=5 ,		negative_prompt="""""" ,		).to_tuple()

            lowerCAmelCase    		=			pipeline(
                image=lowercase ,		mask_image=lowercase ,		image_embeds=lowercase ,		negative_image_embeds=lowercase ,		generator=lowercase ,		num_inference_steps=100 ,		height=768 ,		width=768 ,		output_type="""np""" ,		)

            lowerCAmelCase    		=			output.images[0]

            assert image.shape == (768, 768, 3)

            assert_mean_pixel_difference(lowercase ,		lowercase					)













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






